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IJSTR >> Volume 9 - Issue 12, December 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Deep Learning based OFDM System for Underwater Acoustic Communication with Mitigation of Peak to Average Power Ratio

[Full Text]

 

AUTHOR(S)

Amir Ali, Baowei Chen*, Waleed Raza, Lin Sun, Asif Ali, Adul Hanan Samo

 

KEYWORDS

OFDM, PAPR, Auto-Encoder, Tanh layer.

 

ABSTRACT

The need of fast, reliable and the accuracy in communication networks is increasing day by day. The intelligent networks have left their beneficial impact in underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication system. In the UWA communication network the OFDM technology is employed to get a reliable and robust communication. For obtaining better performance of bit error rate (BER) and significant gain in the system, the deep learning auto-encoders are utilized in deep neural networks (DNN). Considering several benefits of auto-encoders in the OFDM system it goes through high peak to average power ratio (PAPR) that makes power amplifier (PA) operating in nonlinear region. So, to maintain the operation of power amplifier working in linear region, this paper proposes a deep learning based PAPR mitigation method termed as T-AE, PAPR method. Firstly, the PAPR is reduced with a novel T-AE layer in the auto-encoder; secondly the proposed method makes the PA operate in the linear region. Finally, to prove the feasibility of proposed method the simulation is performed which verifies the superiority of our proposed method with efficient performance of BER as compared to traditional OFDM systems.

 

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